Evovest / EvoTrees.jl
Boosted trees in Julia
Why this job: works in the areas we cover, but does not say which job it does
What it does
A Julia implementation of boosted trees with CPU and GPU support. Efficient histogram based algorithms with support for multiple loss functions (notably multi-target objectives such as max likelihood methods).
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Active
- Last commit 12 days ago
- Stars
- 201
- On GitHub
- Licence
- Apache-2.0
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- v0.20.0
- Released 13 days ago
- Built with
- Julia
- 100% of the code
Is it looked after?
- Contributors
- 6
- Last 90 days
- Issues
- 82% closed
- 19 still open
- Pull requests
- 267 merged
- 11 waiting
- Releases
- 92
- 10 in the last year
- No contributing guide
- No security policy
- No code of conduct
Similar repos
| Repo | Stars | Last commit | Licence | Downloads for |
|---|---|---|---|---|
| lightgbm-org / LightGBM | 18,970 | 2 days ago | MIT | Mac, Windows, Linux |
| JuliaML / LossFunctions.jl | 153 | 46 days ago | Custom | No downloads |
| FluxML / Metalhead.jl | 354 | 4 days ago | Custom | No downloads |
More: languages, topics and project record
Languages
- Julia 100%
Topics
- gradientboosting
- machine-learning
- julia
- gradient-boosting
- gbrt
- decision-tree
- regression
- quantile
- boosted-trees
- poisson
- logistic
Project record
- JobOther AI tools
- StatusListed
- Commits1,199
- Forks28
- Watching5
- Stars in 28 days0
- CreatedJan 8, 2019
- First seen by usNov 7, 2026
- CheckedNov 8, 2026